Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India

International Journal of Environmental Research and Public Health

Article Applying Integrated Exposure-Response Functions to PM2.5 Pollution in India

Vijay S. Limaye 1,2,* , Wolfgang Schöpp 3 and Markus Amann 3

1 Nelson Institute for Environmental Studies, Center for Sustainability and the Global Environment (SAGE), University of Wisconsin-Madison, Madison, WI 53726, USA 2 Department of Population Health Sciences, University of Wisconsin-Madison, Madison, WI 53726, USA 3 International Institute for Applied Systems Analysis, 2361 Laxenburg, Austria; [email protected] (W.S.); [email protected] (M.A.) * Correspondence: [email protected]; Tel.: +1-212-727-4683; Fax: +1-212-727-1773

Received: 7 November 2018; Accepted: 18 December 2018; Published: 26 December 2018

Abstract: Fine particulate matter (PM2.5, diameter ≤2.5 µm) is implicated as the most health-damaging air pollutant. Large cohort studies of chronic exposure to PM2.5 and mortality risk are largely confined to areas with low to moderate ambient PM2.5 concentrations and posit log-linear exposure-response functions. However, levels of PM2.5 in developing countries such as India are typically much higher, causing unknown health effects. Integrated exposure-response functions for high PM2.5 exposures encompassing risk estimates from ambient air, secondhand smoke, and active smoking exposures have been posited. We apply these functions to estimate the future cause-specific mortality risks associated with population-weighted ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. The loss in statistical life expectancy (SLE) is calculated based on risk estimates and baseline mortality rates. Losses in SLE are aggregated and weighted using national age-adjusted, cause-specific mortality rates. 2030 PM2.5 pollution in India reaches an annual mean of 74 µg/m3, nearly eight times the corresponding World Health Organization air quality guideline. The national average loss in SLE is 32.5 months (95% Confidence Interval (CI): 29.7–35.2, regional range: 8.5–42.0), compared to an average of 53.7 months (95% CI: 46.3–61.1) using methods currently applied in GAINS. Results indicate wide regional variation in health impacts, and these methods may still underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response functions suggests that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality.

Keywords: air pollution; exposure-response; modeling; health impact assessment; India

1. Introduction A wealth of epidemiological research over the past two decades indicates that both acute and chronic exposure to ambient air pollution is associated with adverse health effects [1]. Fine particulate matter (PM2.5, particles of aerodynamic diameter ≤2.5 microns) is consistently implicated as the most damaging pollutant to human health. The World Health Organization (WHO) has estimated that ambient air pollution contributes to 3.2 million premature deaths and 76,163 disability adjusted life years annually, largely due to the impacts of cardiovascular disease [2]. Of this staggering total, two-thirds of the burden falls in Asia, where PM2.5 concentrations are some of the world’s highest [3]. Fossil fuel combustion contributes the majority of hazardous PM2.5, at the same time generating earth-warming greenhouse gases. The Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model identiﬁes emission control strategies that maximize co-beneﬁts from the control of local

Int. J. Environ. Res. Public Health 2019, 16, 60; doi:10.3390/ijerph16010060 www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2019, 16, 60 2 of 20 air quality, speciﬁcally in mitigation of greenhouse gas emissions and reduced human mortality from exposures to air pollution. GAINS utilizes a detailed spatial and sector-speciﬁc emissions inventory to calculate annual average ambient concentrations of PM2.5 with the use of source-receptor relationships ◦ from the TM5 model [4], which calculates PM2.5 concentrations at a 1 spatial resolution. The TM5 approach is augmented by a special routine to identify sub-grid differences in PM2.5 concentrations as a function of local emission densities and the spatial extensions of urban areas within each grid cell. Through this method, GAINS applies population data from the CIESIN 2.5◦ database to calculate an urban increment of PM2.5 that can be applied to major population centers [4]. The Indian subcomponent of GAINS encompasses 553 1◦ grid cells, and also considers transboundary air pollution spanning neighboring countries. PM2.5 pollution in India is primarily sourced to fuel combustion in non-industrial plants and the manufacturing industry [5]. Another important source is solid fuel combustion for cooking and heating in homes; indoor air pollution is a significant source of all air-related health impacts in the country [6–8]. Increasing industrialization, urbanization, population growth, and demand for transportation also exacerbate PM2.5 concentrations. While the link between indoor PM2.5 exposures and health effects is well established [7], there remains a dearth of evidence on the specific sources and chemical properties of PM2.5 that are most hazardous [9].

1.1. Current GAINS Health Impact Assessment Methodology Prospective cohort studies largely confined to Western countries provide the epidemiological basis for health impact assessments of PM2.5 [10,11]. The GAINS model includes a health impact module that quantifies the effects of chronic exposure to air pollutants (ozone and PM2.5) on reduced life expectancy for adults aged 30 and older [12]. Due to the lack of region-specific epidemiologic evidence identifying associations with mid- and high-level PM2.5 exposures and health impacts, GAINS applies the PM2.5 relative risk function for all-cause mortality derived by Pope et al., (2002) [13], a robust estimate that is in agreement with other long-term studies. However, this methodology does not constrain the accumulation of health impacts related to PM2.5 exposures at any level, contrary to the evidence from proxy exposures of a plateau in relative risk the due to a combination of factors (see Section 1.3)[14–20]. Therefore, the health impact estimates from GAINS and other models like it may not accurately reflect the human health toll of air pollution in highly polluted settings. The limitations of current extrapolation techniques are a focus of attention from pollution regulators and researchers in India, who have called for more investigation into the particular exposures and vulnerabilities of populations in the region [21,22].

1.2. All-Cause vs. Cause-Speciﬁc Mortality

A review of PM2.5-related health studies provides estimates of log-linear concentration-response functions for three categories of cause-specific mortality: cardiovascular disease (ischemic heart disease and cerebrovascular disease/stroke), respiratory disease (acute lower respiratory infection and chronic obstructive pulmonary disease), and lung cancer [3,23]. The relative risks associated with PM2.5 exposure as specified in the American Cancer Society’s Cancer Prevention Study-II (CPS-II) are indicated below in Table1. Compared to the all-cause risk estimate, the relative risk values are higher for cardiovascular disease and lung cancer, due to the fact that the baseline annual mortality incidence rates for these specific outcomes are by definition lower than annual all-cause mortality incidence (see Table2 for estimates of cause-specific mortality rates in India). Int. J. Environ. Res. Public Health 2019, 16, 60 3 of 20

3 Table 1. Relative risk factors for speciﬁc causes of death for a 1 µg/m change in PM2.5 exposure from the American Cancer Society Cancer Prevention Study-II [3,24].

Relative Risk Cause of Death 95% Conﬁdence Interval (Central Estimate) All-Cause 1.006 1.002–1.100 Cardiovascular Disease 1.017 1.011–1.024 (Ischemic Heart Disease and Stroke) Respiratory Disease (Chronic Obstructive Pulmonary Disease 1.006 0.097–1.016 and Acute Lower Respiratory Infection) Lung Cancer 1.014 1.006–1.023

1.3. Risk at High Levels of Pollution

Most prospective cohort studies exploring health risks from chronic exposure to PM2.5 have been conducted in developed countries, where ambient PM2.5 concentrations commonly range between 3 5–30 µg/m . However, PM2.5 levels in developing countries commonly exceed this range [25,26], and current health impact estimation techniques in GAINS do not adequately adjust for this important difference. Estimates of urban air pollution derived from direct monitoring [27,28] and satellite measurements of aerosol optical depth [29,30] indicate that PM2.5 levels in Indian cities routinely exceed levels deemed unsafe for human health, according to the country’s national ambient air quality standards and intermediate guideline levels recommended by the WHO [9,27,31–33]. To date, no large epidemiological cohort studies have estimated the effect of chronic exposure to PM2.5 on risk of death at these high concentrations, though state-specific estimates of health impacts using statistical modeling have recently been published [8]. The most recent Global Burden of Disease (GBD) analysis continued the cause-specific analysis to hypothesize about the relative risk functions at higher doses of PM2.5 in ambient air. The assessment posited integrated exposure-response functions for specific causes of death [2]. Under this approach, the source and precise chemical composition of fine particles is of secondary concern to the absolute quantity of inhaled pollution; as a result, health impacts due to exposure from ambient air, secondhand smoke, and active cigarette smoking can be aggregated and analyzed along a unified exposure-response curve [20,34,35]. The integrated exposure-response model is posited because the risk of death is not seen to rapidly increase across the range of human exposures, as would be implied by the current log-linear relative risk methodology implemented in GAINS and other related models [20,34,36]. The integrated model is consistent with a biological saturation hypothesis for exposures to fine particles and the mechanisms underlying respiratory and cardiovascular disease [14–16]. While PM2.5 is regulated by mass concentration, studies indicate that the chemical composition of inhaled pollution (especially transition metals, organic compounds, semiquinones, and endotoxins) also directly influences cardiovascular risks [37]. Despite mounting toxicological evidence lending credence to the saturation hypothesis, the observed risk pattern across the range of documented human exposures to PM2.5 may also reflect exposure misclassification at high PM2.5 levels, competing mortality risks at high levels, and/or decreased inhalation rates for the heaviest of smokers that skew risk estimates downward [34]. While recent evidence implicates specific chemical components of PM2.5 in health risks [38], the dearth of species-specific knowledge in exposure patterns (from emissions and atmospheric modeling) and health risks (from epidemiologic studies) precludes a comprehensive analysis of the collective impact of PM2.5 constituents in a health impact assessment framework. This study aims to better quantify the effects of applying the integrated exposure-response functions on life expectancy in India by illuminating effects beyond average national impacts. By better spatially characterizing future risks associated with exposures to high levels of PM2.5 based on specific health endpoints, this work responds to calls for a more context-specific approach to health impact assessment for air pollution in India [21,22,39,40]. Int. J. Environ. Res. Public Health 2019, 16, 60 4 of 20

2. Materials and Methods Int. J. Environ. Res. Public Health 2018, 15, x 4 of 21 Estimates of air pollution-related health impacts are calculated using three sets of relative risk factors: the2. Materials log-linear and all-cause Methods premature mortality estimate from Pope et al., (2002) [24], the log-linear cause-specificEstimates premature of air mortality pollution-related estimate health from impacts CPS-II, are andcalculated the integrated using three cause-specificsets of relative risk premature mortalityfactors: functions the log-linear posited all-cause in the premature GBD assessment. mortality estimate These from three Pope approaches et al., (2002) [24], for the estimating log- the linear cause-specific premature mortality estimate from CPS-II, and the integrated cause-specific health impacts of chronic exposure to PM2.5 are compared to demonstrate the range of uncertainty premature mortality functions posited in the GBD assessment. These three approaches for estimating characteristic of health impact assessments. Specifically, as shown in Figure1, exposure scenarios for the health impacts of chronic exposure to PM2.5 are compared to demonstrate the range of uncertainty India in GAINScharacteristic are utilized of health toimpact estimate assessments. the population Specifically, health as shown impact in Figure of PM 1, 2.5exposurepollution scenarios in 2030. for Health impacts areIndia estimated in GAINS both are utilized in reduced to estimate statistical the population life expectancy health impact (SLE) andof PM cumulative2.5 pollution yearsin 2030. of life lost (YOLL).Health This work impacts explores are estimated the impacts both in reduced of both statis thetical newly-proposed life expectancy (SLE) shape and of cumulative the exposure-response years functionof as life well lost (YOLL). as the disaggregationThis work explores ofthe all-cause impacts of mortalityboth the newly-proposed analysis into shape mortality of the exposure- estimation for response function as well as the disaggregation of all-cause mortality analysis into mortality specific causesestimation of death.for specific causes of death.

All-Cause, Log-Linear Loss in Statistical Exposure-Response Function Life Expectancy GAINS PM Age- 2.5 Relative (SLE) Exposure Adjusted Risk Point Estimates by Mortality Estimates Grid Cell Cause-Specific, Log-Linear Rates (Baseline, 2030 Population (Baseline, Exposure-Response Functions (All-Cause 2020, and 2020, and and Cause- 2030) 2030) Cause-Specific, Integrated Specific) Years of Life Lost Exposure-Response (YOLL) Power Functions

Figure 1. Steps in a comparative health impact analysis utilizing PM2.5 exposure estimates for India Figure 1. Steps in a comparative health impact analysis utilizing PM2.5 exposure estimates for India from the Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model, three distinct from the Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model, three distinct exposure-response functions, and baseline population mortality rate estimates. Through this exposure-responsesequence, we functions, estimate losses and in baseline statistical population life expectancy mortality (SLE) and rate years estimates. of life lost Through (YOLL) due this to sequence, we estimateambient losses exposures in statistical in 2030. life expectancy (SLE) and years of life lost (YOLL) due to ambient exposures in 2030. 2.1. Baseline Mortality Rates 2.1. Baseline Mortality Rates In order to calculate changes in mortality patterns due to chronic exposure to PM2.5, annual cause-specific mortality rates were first calculated. WHO cause-specific mortality data for India in In order to calculate changes in mortality patterns due to chronic exposure to PM2.5, annual cause-specific2008 were mortality used for ratestwo age were groups first (15–59 calculated. and 60–100) WHO and three cause-specific categories of mortalitydeath: lung datacancer for India (malignant neoplasms of the trachea, bronchus, and lung), cardiovascular disease (ischemic heart in 2008 weredisease usedand cerebrovascular for two age groupsdisease), (15–59and respiratory and 60–100) disease and (chronic three obstructive categories pulmonary of death: lung cancer (malignantdisease, and neoplasmsacute lower respiratory of the trachea, infection) bronchus, [41]. Table and 2 includes lung), cardiovascularthese values along diseasewith their (ischemic heart diseasepopulation and cerebrovasculardenominators: disease), and respiratory disease (chronic obstructive pulmonary disease, and acute lower respiratory infection) [41]. Table2 includes these values along with their Table 2. Estimated deaths in India (2008) by cause [41]. Age-specific 2008 population population denominators. estimates for India from the International Data Base (IDB) of the U.S. Census Bureau [42]. *Population estimate for population aged 30–59, used in age-adjusted mortality rates for Table 2. populationEstimated aged deaths 30–100 in India (Table (2008) 3). by cause [41]. Age-specific 2008 population estimates for India from the International Data Base (IDB) of the U.S. Census Bureau [42]. * Population estimate for Age Mortality Total population aged 30–59,Cause used of Premature in age-adjusted Mortality mortality rates for population aged 30–100Population (Table 3). Group Estimate Deaths Ischemic Heart Disease 672,000 Age Group Cause of Premature Mortality Mortality Estimate Total Deaths Population Stroke 180,200 720,694,000 15–59 IschemicAcute Lower Heart Respiratory Disease Illness 672,000 214,400 3,092,700 Stroke 180,200 (391,440,200*) Chronic Obstructive Pulmonary Disease 44,900 720,694,000 15–59 Acute Lower Respiratory Illness 214,400 3,092,700 Lung Cancer 20,600 (391,440,200*) ChronicIschemic Obstructive Heart Disease Pulmonary Disease 44,900826,000 LungStroke Cancer 20,600646,400 60–100 IschemicAcute Lower Heart Respiratory Disease Illness 826,000 870,300 4,678,400 90,312,849 StrokeChronic Obstructive Pulmonary Disease 646,400 220,000 60–100 AcuteLung Lower Cancer Respiratory Illness 870,300 31,500 4,678,400 90,312,849 Chronic Obstructive Pulmonary Disease 220,000 Lung Cancer 31,500

The total number of estimated deaths for each specific cause was divided by the annual estimate of total deaths in each age group to arrive at an age-specific, cause-specific annual mortality rate. Int. J. Environ. Res. Public Health 2019, 16, 60 5 of 20

The mortality rates for the age 15–59 group were scaled to reflect the overall mortality rate for all adults above age 30 using weighting based on age-specific population counts (see Section 2.5). Age-specific mortality rates for the two age-groups were summed and weighted by the 2008 population shares of each group (30–59 year olds and 60–100 year olds) to arrive at an annual age-adjusted cause-specific proportion of the total mortality rate (Table3). The adjusted rates were applied uniformly to the above-30 population. For the log-linear cause-specific estimates, age-adjusted mortality rates were consolidated into three categories: cardiovascular disease (ischemic heart disease and stroke), respiratory disease (chronic obstructive pulmonary disease and acute lower respiratory infection), and lung cancer.

Table 3. Annual age-adjusted proportion of mortality rate for India in 2008 for adults aged 30–100 in 2008.

Cause of Premature Mortality Age-Adjusted Share of Annual Mortality Rate (%) Ischemic Heart Disease 21.0 Acute Lower Respiratory Illness 9.1 Stroke 7.3 Chronic Obstructive Pulmonary Disease 2.1 Lung Cancer 0.7

Age-adjusted mortality shares of mortality rates for air pollution-related disease indicate that ischemic heart disease accounts for the majority of deaths, followed by acute lower respiratory infections, stroke, chronic obstructive pulmonary disease, and lung cancer.

2.2. Assumptions in GAINS

Health impact assessment for PM2.5 in GAINS assumes human exposure to PM2.5 from both primary sources (black carbon, organic carbon, other organic matter, and mineral dust) and secondary inorganic aerosols formed from the emissions of SO2, NOX, and NH3. No health impacts are quantified for exposure to PM2.5 stemming from natural sources and secondary organic aerosols, as it is assumed that these emissions are not as amenable to human interventions. Moreover, adverse health effects are not quantified for concentrations lower than 7 µg/m3; although there is not convincing evidence that a safe threshold for PM2.5-related health effects exists [43–47], this level is consistent with the GBD counterfactual concentration of 5.8–8.8 µg/m3 used for health impact estimates [2,47–49]. In urban areas, health effects are correlated with annual mean urban background levels. Health impacts and baseline mortality rates are calculated for the exposed population aged 30–100 years. This calculation assumes that all individuals will remain exposed to the exposure level calculated for the rest of their lifetimes.

2.2.1. Point Estimates of Relative Risk

GAINS estimates long-term health impacts from PM2.5 exposure based on a single, large-scale cohort study estimating a concentration-response function for all-cause mortality [24]. Such studies utilize the Cox proportional hazards model, which relates changes in a stress variable (here, PM2.5 concentration) to a proportional increase in the underlying hazard (here, mortality rate) by a proportionality factor (also known as the relative risk) [50]. This model expresses the number of fatalities in a time period Y (usually deﬁned as one year) as a function of baseline mortality rates (Y0), 3 PM2.5 concentrations, and the relative risk factor (β) for an exposure (PM) in µg/m is:

β*PM Y = Y0 * e

In this model, the annual baseline death rate changes as a function of level of PM2.5 exposure, and the associated relative risk (RRLOGLINEAR) is:

RRLOGLINEAR (PM) = exp(β*PM) Int. J. Environ. Res. Public Health 2019, 16, 60 6 of 20

Int. J. Environ. Res. Public Health 2018, 15, x 6 of 21 While β is small and behaves quasi-linearly in the exposure range studied in the United States (average of 17.7, SD of 3.7 µg/m3), the GBDRRLOGLINEAR assessment (PM) = posited exp(β*PM) a power function for the relative risk pattern, soWhile that riskβ is ofsmall death and increases behaves quasi-linearly at marginally in lower the exposure rates as range PM2.5 studiedconcentrations in the United increase States (Figure2)[(average20,24]. of For 17.7, the SD power of 3.7 μ functiong/m3), the model, GBD assessment risk plateaus posited at a high power exposures function for and the the relative posited risk pattern, so that risk of death increases at marginally lower rates as PM2.5 concentrations increase (Figure relative risk (RRPOWER) is: 2) [20,24]. For the power function model, risk plateaus at high exposures and the posited relative risk for PM < PMcf: RRPOWER (PM) = 1 (RRPOWER) is: for PM > PM : RR (PM) = 1 + α(1 − exp−γ [(PM− PMcf )δ]) cf POWERfor PM < PMcf: RRPOWER (PM) = 1 3 where PMcf is the counterfactualfor PM concentration > PMcf: RRPOWER (7(PM)µg/m = 1 + α,(1 Section – exp[– γ2.2 (PM–) in PMcf) GAINSδ]) below which no additional health risk is conferred. For this model, γ indicates the RR ratio comparing low to high where PMcf is the counterfactual concentration (7 μg/m3, Section 2.2) in GAINS below which no exposure scenarios.additional health The power risk is ofconferred. PM concentration For this model, term γ (indicatesδ) is used the in RR order ratio to comparing better estimate low to high risk over a wideexposure range ofscenarios. exposures, The andpower we of apply PM concentration estimates for termα, γ (,δ and) is usedδ from in order Burnett to better et al., estimate (2014) [ 20risk], each derivedover using a wide nonlinear range of exposures, regression and methods. we apply estimates for α, γ, and δ from Burnett et al., (2014) [20], each derived using nonlinear regression methods. 4

3.5

COPD 2.5 ALRI STR LC 2 IHD Log (Relative Risk) Log (Relative

1.5

1 0 50 100 150 200 3 PM2.5 (µg/m )

Figure 2. Integrated exposure-response relative risk estimates for PM2.5 exposure (ALRI, acute lower Figure 2. Integrated exposure-response relative risk estimates for PM2.5 exposure (ALRI, acute lower respiratoryrespiratory infection; infection; COPD, chronic COPD, obstructivechronic obstructive pulmonary pulmonary disease; disease; IHD, IHD, ischemic ischemic heart heart disease; disease; LC, LC, 3 lung cancer;lung STR, cancer; stroke) STR, [20 stroke)]. For [20] concentrations. For concentrations below below 7 µg/m 7 μg/m, log-relative3, log-relative risk risk is assumedis assumed to to be be 1. 1.

In comparisonIn comparison to the all-cause to the all-cause mortality mortality relative relative risk estimaterisk estimate currently currently deployed deployed in in GAINS, GAINS, thethe integratedintegrated exposure-response exposure-response power power functions functions for specific for specific causes causes of death of death are are considerably considerably steeper steeper in slope atin low slope exposure at low exposure levels. levels. However, However, the baselinethe baselin mortalitye mortality rate rate forfor eacheach specific specific disease disease is by is by definitiondefinition lower lower than thethan all-cause the all-cause population population mortality mortality rate. rate. Therefore, Therefore, it is not clear clear whether whether a a cause-specificcause-specific health impacthealth impact assessment assessment of air of pollution-related air pollution-related disease disease results results in in lower lower or or higherhigher estimates dueestimates to these due compensating to these compensating factors. From factors. these Fr models,om these point models, estimates point ofestimates risk were of calculated risk were calculated at baseline (natural background), 2020, and 2030 PM2.5 levels. at baseline (natural background), 2020, and 2030 PM2.5 levels.

2.2.2. Incremental Risk Once point estimates of relative risk were determined, incremental risk slopes were calculated to linearly approximate the concentration-response power functions between the three points. Slopes were log-transformed for application in later calculations of SLE and for comparison with parameter estimates in the current literature. The general formula for the incremental risk value (R) is: Int. J. Environ. Res. Public Health 2019, 16, 60 7 of 20

R = Log (RR1 − RR2 + 1)/ ∆PM where the RR subscripts indicate point estimates of relative risk for speciﬁc causes of death for scenarios considered in pairs (baseline PM2.5 levels and 2020 levels, 2020 levels and 2030 levels).

2.2.3. Urban and Rural Shares of Pollution

GAINS accounts for spatial heterogeneity of PM2.5 levels within each grid cell through its incorporation of urban shares for the year 2030. As a result, model predictions include PM2.5 levels for both rural and urban areas within each grid (these values coincide if the urban share is zero), enhancing geographic resolution of exposure assessment compared to past approaches. For estimates of relative risk, separate rural and urban PM2.5 estimates were utilized. Estimates of grid-averaged SLE were based on weighting of relative risks using the urban share fraction.

2.3. Scenario Selection

GAINS modeling of future PM2.5 levels in India is conservative because it only incorporates emissions trends but does not explicitly calculate future climate change impacts on pollution, even though climate change could exacerbate surface concentrations of this pollutant [51,52]. Global climate modeling suggests a future increase in the frequency of stagnant air masses for mid-latitudes worldwide, suggesting further accumulation of fine particles [53,54]. Moreover, changes in north-south gradients of sea surface temperatures could inhibit summer monsoon circulation and thus decrease rainfall (a key driver of particle deposition) over land [51]. In addition to direct effects of climate forcing on reduced precipitation rates, satellite data reveal that urban and industrial air pollution can suppress precipitation, as polluted plumes are comprised of many, but inefficiently small, cloud condensation nuclei [55,56]. These findings support the view that PM2.5 pollution will remain an important air quality threat in India even if emissions do not drastically rise in the future. In order to estimate health impacts in 2030 in light of the integrated exposure-response functions, we used GAINS estimates of annual PM2.5 concentrations in each grid under baseline natural background conditions, in 2020 under a maximum feasible reduction scenario, and in 2030 under a business-as-usual emissions scenario. Point relative risk values were calculated for each of these three PM2.5 levels, and log-linear estimates of risk were calculated for use in incremental life expectancy calculations. Choice of the intermediate scenario for 2020 allowed us to better capture the shape of the revised exposure-response power function with log-linear slope estimates between point estimates of relative risk. In contrast to the current method, the integrated exposure-response functions display higher slopes at lower levels of PM2.5, and flatter slopes as high levels. The point estimate with the intermediate 2020 value also reduces error in approximating the revised curve with piecewise log-linear functions. In grid cells with urban populations, an average annual PM2.5 level for the entire grid was calculated by summing the urban and rural shares and weighting the urban PM2.5 level by the urban fraction factor. The use of this piecewise function allowed us to capture health impacts at lower exposure levels, where the new exposure-estimates suggest that incremental health impacts are proportionally higher. Moreover, the choice of these scenarios for 2020 and 2030 represents a realistic estimate of future Indian air pollution levels and therefore a reasonable case study for health impact analysis. We also calculated SLE impacts using both log-linear, all-cause and log-linear, cause-specific models, each comparing 2030 pollution levels to baseline natural background levels. The log-linear form of these models obviates the need for 2020 pollution and risk point estimates.

2.4. Statistical Life Expectancy Changes in SLE in India due to increased pollution between baseline natural background levels and 2020 and between 2020 levels and 2030 levels were calculated for each of five specific outcomes implicated in air pollution-related mortality. The basic methodology follows the approach Int. J. Environ. Res. Public Health 2019, 16, 60 8 of 20 recommended by the Task Force on Health [57]. This method uses cohort- and country-specific mortality data to estimate a survival function over time. This function is modified by exposure to PM2.5 pollution, in a relationship that can be simplified to losses in SLE for an individual. The calculation uses life table analysis and an approximation method for the calculation of the change in SLE [58]. These life expectancy values were weighted by their age-adjusted mortality rates, derived from WHO data [41]. Changes in SLE were calculated separately for each cause-specific outcome, and then multiplied by proportional factors from Table3 and summed to arrive at cumulative estimates of changes in SLE for 2030.

2.5. Years of Life Lost Because of spatial heterogeneity in the distribution of air pollution and human populations, it is important to assess the total burden of air pollution on society in addition to impacts on SLE. To assess the cumulative burden of PM2.5 air pollution on the Indian population, we calculated the YOLL in each grid [59]. We multiply the loss in SLE (expressed as fractions of years) by the total population (age 30–100) in each grid cell as currently estimated by GAINS. Population estimates for India from the International Data Base (IDB) of the U.S. Census Bureau were used to calculate a proportional factor of above-30 population in 2030 [42]. This factor (0.422) was applied to population estimates from GAINS to reﬂect the above-30 population total in each grid cell. In the YOLL estimate, we applied national-level age distribution data to calculate the total number of exposed individuals within each grid.

3. Results

Application of the three sets of exposure-response functions to GAINS-estimated PM2.5 concentration data led to health impact estimates for both changes in SLE impacts and cumulative YOLL in the Indian population.

3.1. National Health Impacts

3 PM2.5 pollution is predicted to reach an annual mean of 74 µg/m in 2030, nearly eight times the corresponding WHO air quality guideline [10]. Nationally, the average loss in SLE (aggregated from cause-specific outcomes) is 32.5 months (95% Confidence Interval: 29.7–35.2), compared to an average of 53.7 months (95% CI: 46.3–61.1) currently estimated in GAINS using the log-linear, all-cause mortality risk estimate. Table4 compares the health impacts estimated by the single all-cause and two cause-specific exposure-response functions.

Table 4. Changes in statistical life expectancy (SLE) using log-linear= all-cause, log-linear cause-specific,

and power function cause-specific exposure-response functions for premature mortality and GAINS PM2.5 exposure estimates. National averages calculated from population-based weighting of individual grids.

Average National Loss in SLE 95% Confidence Interval Exposure-Response Function (Months) (Months) Log-Linear, All-Cause 53.7 46.3–61.1 Log-Linear, Cause-Specific 49.4 42.7–56.1 Power Function, Cause-Specific 32.5 29.7–35.2

3.2. Regional Health Impacts The different SLE impacts estimated by the three models are depicted for each GAINS region in Figure3. Regions are sequenced from lowest to highest PM 2.5 level in 2030. Figure4 displays the aggregation of cause-speciﬁc SLE impacts using the power function. Int. J. Environ. Res. Public Health 2018, 15, x 9 of 21

3.2. Regional Health Impacts The different SLE impacts estimated by the three models are depicted for each GAINS region in Int. J. Environ. Res. Public Health 2019, 16, 60 9 of 20 Figure 3. Regions are sequenced from lowest to highest PM2.5 level in 2030. Figure 4 displays the aggregation of cause-specific SLE impacts using the power function.

FigureFigure 3. Months 3. Months of of SLE SLE lost lost due due to to chronic chronic exposureexposure to to PM PM2.52.5 basedbased on onthree three exposure-response exposure-response functions:functions: Power Power Function Function C-SC-S (cause-specific, (cause-speciﬁc, blue blue),), Log-Linear Log-Linear C-S (cause-specific, C-S (cause-speciﬁc, green), and green), Log- and Int. J. Environ. Res. Public Health 2018, 15, x 10 of 21 Log-LinearLinear A-C A-C (all-cause, (all-cause, red). red). Regions Regions are sequenced are sequenced from fromlowest lowest to highest to highest PM2.5 level PM in2.5 2030.level in 2030.

FigureFigure 4. Months 4. Months of SLEof SLE lost lost in in GAINS GAINS regions regions due to to 2030 2030 PM PM2.52.5 exposures,exposures, applying applying cause-specific cause-speciﬁc integratedintegrated exposure-response exposure-response power power functionsfunctions (I (IHD,HD, ischemic ischemic heart heart disease; disease; ALRI, ALRI, acute acute lower lower respiratoryrespiratory infection; infection; STR, STR, stroke; stroke; COPD, COPD, chronic chronic obstructive pulmonary pulmonary disease; disease; LC, LC,lung lung cancer). cancer).

Int. J. Environ. Res. Public Health 2019, 16, 60 10 of 20 Int. J. Environ. Res. Public Health 2018, 15, x 11 of 21

3.3. Grid Comparison Figure5 5displays displays the the contrast contrast in SLE in estimatesSLE estima fortes each for grid each cell grid in GAINS cell in between GAINS the between integrated the integratedexposure-response exposure-response power function power and function the log-linear and the function.log-linear Forfunction. the same For levelthe same of 2030 level pollution, of 2030 pollution,SLE impacts SLE peak impacts at a much peak at lower a much level lower using level the powerusing the function. power function.

Figure 5. ComparisonComparison of loss in in statistical statistical life expect expectancyancy (SLE, in months) estimated for the year 2030 due to PM pollution in each of 553 1◦ GAINS grid cells, applying the cause-speciﬁc power function due to PM2.5 pollution in each of 553 1° GAINS grid cells, applying the cause-specific power function ◦ (horizontal axis) and all-cause log-linear function (vertical(vertical axis) exposure-response functions. The 4545° line of parity is also indicated. 3.4. Years of Life Lost 3.4. Years of Life Lost Table5 displays the YOLL results for each GAINS region, applying both the power and log- Table 5 displays the YOLL results for each GAINS region, applying both the power and log- linear functions. linear functions.

Table 5. Years of Life Lost (YOLL) estimated for the year 2030 due to PM2.5 pollution in India using Tableintegrated, 5. Years cause-speciﬁc of Life Lost exposure-response (YOLL) estimated functionsfor the year compared 2030 due to to the PM current2.5 pollution (log-linear, in India all-cause) using integrated,GAINS approach. cause-specific Regional exposure-response averages calculated function from population-baseds compared to the weighting current (log-linear, of individual all-cause) grids. GAINS approach. Regional averages calculated from population-based weighting of individual grids. YOLL YOLL Population Region (PowerYOLL Function, (Log-Linear,YOLL All-Cause AgePopulation 30–100 Region Cause-Speciﬁc(Power Function, Mortality) (Log-Linear,Mortality) Age 30–100 Andaman & Nicobar 100,330Cause-Specific 71,041 Mortality) All-Cause Mortality) 75,405 AndhraAndaman Pradesh & Nicobar 31,334,161 100,330 71,041 76,806,481 75,405 105,513,079 ArunachalAndhra Pradesh Pradesh 31,334,161626,257 76,806,481 852,835 105,513,079 1,168,634 ArunachalAssam Pradesh 10,700,667 626,257 852,835 29,603,288 1,168,634 45,577,660 BiharAssam 33,518,25310,700,667 29,603,288 105,402,006 45,577,660 177,643,919 ChandigarhBihar 33,518,253 188,466 105,402,006 593,341 177,643,919 1,052,555 ChhattisgarhChandigarh 8,681,291188,466 593,341 24,396,711 1,052,555 35,680,365 Dadra &Chhattisgarh N. Haveli 8,681,291 48,125 24,396,711 100,420 35,680,365 118,675 Daman & Diu 48,744 85,837 94,496 Dadra & N. Haveli 48,125 100,420 118,675 Delhi 4,988,542 17,486,999 43,833,733 GoaDaman & Diu 528,426 48,744 85,837 962,794 94,496 1,061,004 GujaratDelhi 20,791,0474,988,542 17,486,999 43,259,945 43,833,733 54,757,350 HaryanaGoa 9,389,899528,426 962,794 28,457,447 1,061,004 56,689,670 Himachal PradeshGujarat 2,604,42820,791,047 43,259,945 6,119,326 54,757,350 7,806,830 Jammu &Haryana Kashmir 4,047,0449,389,899 28,457,447 9,127,280 56,689,670 12,470,991 JharkhandHimachal Pradesh 11,598,975 2,604,428 6,119,326 38,033,918 7,806,830 72,210,222 Jammu & Kashmir 4,047,044 9,127,280 12,470,991 Jharkhand 11,598,975 38,033,918 72,210,222 Karnataka 21,664,583 43,394,208 50,991,253 Kerala 13,238,535 28,926,694 36,050,459

Int. J. Environ. Res. Public Health 2019, 16, 60 11 of 20

Table 5. Cont.

YOLL YOLL Population Region (Power Function, (Log-Linear, All-Cause Age 30–100 Cause-Speciﬁc Mortality) Mortality) Karnataka 21,664,583 43,394,208 50,991,253 Kerala 13,238,535 28,926,694 36,050,459 Madhya Pradesh 24,900,682 58,026,813 74,456,342 Maharashtra 38,349,724 89,336,917 118,150,353 Manipur 954,847 2,538,910 4,224,297 Meghalaya 1,176,235 3,702,158 5,955,767 Mizoram 359,130 877,077 1,168,562 Nagaland 717,811 1,330,132 1,721,418 Orissa 15,167,417 47,199,759 79,501,011 Pondicherry 55,121 90,030 103,332 Punjab 10,172,018 31,863,412 60,150,281 Rajasthan 23,317,953 45,789,791 69,256,961 Sikkim 241,827 356,728 435,056 Tamil Nadu 25,184,943 49,909,735 60,974,345 Tripura 1,314,249 4,179,375 6,652,071 Uttar Pradesh 68,524,470 225,900,339 450,382,887 Uttarakhand 3,574,632 9,192,348 13,715,040 West Bengal 32,806,548 114,273,583 233,985,679 National Total 420,915,379 1,106,384,265 1,883,629,702

4. Discussion

4.1. Three Exposure-Response Models

Three models for impacts of PM2.5 on health (reduced life expectancy) are represented in Figure3. These three models (log-linear all-cause, log-linear cause-specific, and power function cause-specific) represent increasingly precise estimates of the cumulative health impacts of PM2.5 pollution. Results from these exposure-response functions align most in areas with relatively low levels of pollution (generally in southern India, as other studies have found, [8]). In the least polluted areas, the difference between the lowest and highest SLE estimates (log-linear cause-specific and log-linear all-cause, respectively) is 0.60 months (in the Andaban and Nicobar islands). However, in areas with poorer air quality, model estimates diverge considerably, with a difference of 63.4 months between the lowest and highest SLE estimates in Delhi (power function cause-specific and log-linear all-cause, respectively). Overall, the central estimate of 32.5 months (2.7 years) of SLE loss in 2030 is consistent with other recent national analyses of present-day air pollution impacts, which have estimated SLE losses ranging from 1.5 years to 5.3 years [8,60–62]. While some of these studies also considered indoor air pollution and ozone (O3) exposures, our finding is comparable to a PM2.5-only life expectancy impact of 3.2 years as calculated by Greenstone et al. [63].

4.2. Incremental Impacts In assessing the consequences of applying revised exposure-response functions, it is important to distinguish between incremental and cumulative health impacts. The posited power function implies that the risk of death is not constant along the continuum of exposure to PM2.5. As a result, adverse health impacts rapidly accrue at the lower end of the exposure spectrum, but increase more slowly as one proceeds to higher levels of exposure. For example, for chronic obstructive pulmonary disease, the log-relative risk approximately triples between 10 µg/m3 and 20 µg/m3, but only increases by 7% between ambient concentrations of 100 µg/m3 and 110 µg/m3. Therefore, it is essential to consider the baseline air quality situation in assessing health beneﬁts that might be achieved from reducing air pollution. The most striking consequences of the revised functions are seen in estimates of these incremental impacts. Given these patterns and future exposure risks [64], it is apparent that, in order Int. J. Environ. Res. Public Health 2019, 16, 60 12 of 20 to achieve the biggest health gains, ambitious actions are needed to signiﬁcantly reduce the overall magnitude of ambient PM2.5 air pollution in India.

4.3. Statistical Life Expectancy The cause-speciﬁc analysis results in health impacts that are on the same order of magnitude but generally lower than estimates using the old methodology (see Figure3). Regional SLE impacts across all causes and ages ranged from 8.5–42.1 months, with a population-weighted mean of 32.5 months (standard deviation of 8.2 months). The regions in Figure3 are sequenced by 2030 PM 2.5 level; in the most polluted regions, the impacts on SLE are (as expected) highest. It is also in these places where the loss in SLE due to lung cancer is highest, accounting for 0.6 months of the total loss in SLE in Delhi. There are, however, interesting exceptions to the general trend of increasing health effects by 2030 3 pollution level. The clearest example is in Rajasthan, where the 2030 PM2.5 level is 74.4 µg/m and cumulative SLE loss is estimated at 23.6 months. While Assam and Tripura are expected to experience similar pollution levels in 2030 (68.9 and 77.7 µg/m3, respectively), the latter regions are anticipated to suffer higher SLE losses (33.2 and 38.2 months, respectively) than Rajasthan. This contrast is due to differences in modeled pollution changes between the baseline natural background concentrations and 2030 business-as-usual scenario. Relative to baseline background levels, PM2.5 levels in Assam increased by 67.5 µg/m3 and Tripura by 75.6 µg/m3, while Rajasthan only increased 45.7 µg/m3. Because natural background pollution is high to begin with in Rajasthan, health impacts due to anthropogenic emissions are relatively lower. The results in Figure3 demonstrate that the divergence between the new exposure-response methodology and current GAINS approach is greatest in areas with high levels of PM2.5 pollution. In Delhi, for example, the revised estimate for SLE impacts is 40% of the current GAINS estimate. By comparison, in the least polluted region (Andaman and Nicobar), the power function estimate is 95% of the SLE impact projected using current GAINS methodology.

4.4. Years of Life Lost Cumulative impacts of population exposures to air pollution are helpful in conveying the total burden of human-driven PM2.5 pollution to policymakers. The YOLL estimate (Table5) places the statistical life expectancy calculation in a human context, taking into account spatial heterogeneity in population density. Just as the cause-speciﬁc life expectancy results are lower than under current GAINS assumptions, when population estimates are applied to calculate cumulative Years of Life Lost (YOLL), the health impacts are diminished. Amongst regions, Andaman and Nicobar had the lowest YOLL toll (71,041) due to its small population and relatively modest PM2.5 levels. Uttar Pradesh had the highest YOLL burden (225,900,339) and Delhi, the location with the highest loss in SLE, accumulated 17,487,000 YOLL by 2030. Overall, 1.1 billion YOLL can be attributed to long-term exposure to ambient PM2.5, or roughly one year for each person in India (including those under age 30).

4.5. Sensitivity Analysis To place revised health impact estimates in the context of current GAINS calculations, a sensitivity analysis was conducted by comparing exposure-response methodology. Overall, the revised method results in consistently lower estimates in India (67% ratio of new method to old) of losses in SLE due to ambient concentrations of PM2.5. Nevertheless, application of the power function in less polluted settings could result in SLE estimates that are larger than those calculated here. Nationally, the revised method projects that chronic exposure to human-driven PM2.5 pollution reduces the average Indian citizen’s life by about 32.5 months (Table4, Figures3 and4). Of this total, the largest contributor to premature mortality is ischemic heart disease (15.0 months, 46.1% of total average life-shortening), followed by acute lower respiratory infection (9.5 months, 29.3%), stroke (5.1 months, 15.5%), chronic obstructive pulmonary disease (2.6 months, 8.1%), and lung cancer (0.3 months, 0.01%). The log-linear method estimated a national loss in life expectancy in 2030 at 53.7 months. However, the national picture obscures important regional variation, reﬂected in the relatively large standard deviation of Int. J. Environ. Res. Public Health 2019, 16, 60 13 of 20 the estimate. At the extremes, the integrated exposure-response method results in a 60% lower health impact estimate in Delhi, and a 5% lower estimate in Andaman and Nicobar. However, alternate estimates utilized in the sensitivity analysis reﬂect the largest population health estimate included in GAINS, utilizing the highest reported relative risk estimate from Pope et al. (2002) [24].

4.6. Trends in Urbanization and Population Growth The rapid pace of economic growth on the Indian subcontinent suggests that air quality issues and their public health impacts will remain central over coming years due to increasing emissions, especially if fossil fuels remain a large portion of India’s energy portfolio [4,52]. The Indian government has championed a goal of maintaining annual economic growth above 8% for the next two decades, which would increase per-capita income by a factor of ﬁve and stimulate an increasing demand for energy [4]. India’s population is increasingly urban, and patterns of air pollution exposure are expected to shift away from indoor air towards problems of ambient PM and O3 in cities [10]. As a result, ﬁne particulate matter pollution may worsen in coming decades due to continuing urban expansion and India’s reliance on coal for electricity generation [65]. By 2030, high levels of PM2.5 are projected to pervade across most of the country, with concentrations in the Ganges Valley increasing to more than 150 µg/m3 [4]. Regional variation in the concentration of PM2.5, combined with an increasingly urbanized Indian population, results in important geographic disparities in health. In 2030, for example, life-shortening in the city of Delhi is estimated at 108 months, in comparison to about 10 months in the Andaman and Nicobar islands [4]. The impact of lower levels of air pollution on life expectancy in India is consistent with patterns in the United States between 2000–2007: in one study, a decrease of 10 µg/m3 in the concentration of PM2.5 was associated with an increase in life expectancy of about four months [48].

4.7. Limitations: Model Assumptions and External Validity The spatial resolution of the GAINS model is a key shortcoming, as combination of 1◦ population and exposure estimates with national incidence data provide a limited degree of spatial specificity. While GAINS provides robust national estimates of air pollution exposures, improvement in the spatial coverage and resolution of ambient air quality and baseline health incidence data is vital for estimating human exposures and health impacts more accurately [66,67]. While restricting the health impact analysis to the age 30+ group is consistent with other studies of this kind [68–71] and GAINS is not equipped to consistently apply age-specific relative risk functions for all health endpoints, this simplification biases health impact estimates. The national-level age-specific population distributions from data extrapolated by the U.S. Census Bureau are useful for describing the country-level impacts of air pollution on life expectancy, but not for spatially-explicit estimates of the future disease burden and intervention opportunities for specific age groups. As an example of this limitation, our broad application of the health impacts of ALRI (Tables2 and3) does not reflect the disproportionate burden of this category of infections on the very young (0–5 years), including a higher burden of YOLL in this age subgroup. This limitation of our study is complicated by the fact that we did not apply exposure-response analysis to other diseases like asthma and pneumonia that also contribute to premature mortality from PM2.5, with important age-specific effects [72,73]. Nevertheless, the relative proportion of ALRI-associated impacts is consistent with a city-specific analysis in Varanasi, India [74]. Stratification of health estimates by age group and consideration of a wider range of impacts for the entire population would allow for a more complete understanding of the cumulative impacts of national PM2.5 exposures. Moreover, this study does not apply the integrated exposure-response functions to estimates of indoor air pollution. Solid fuel combustion is still widespread in India, and a large share of health impacts from outdoor exposure to PM2.5 has been linked to this source [4,75]. Future research efforts using GAINS could more fully consider the health impacts of both indoor and outdoor air pollution to better quantify the burden for both children and adults. The integrated exposure-response function Int. J. Environ. Res. Public Health 2019, 16, 60 14 of 20 may be deployed in health impact analyses of indoor air pollution exposures, although the exposure patterns for women and children may be distinct from those for ambient air pollution. In addition to causing premature mortality, the morbidity impacts of PM2.5 pollution are well-documented [76]. These effects include irregular heartbeat, aggravated asthma, airway irritation, and decreased lung function. None of these impacts are addressed under current GAINS methodology despite their contribution to ill health and reduced human productivity. Moreover, exposure to PM2.5 is known to cause adverse health impacts on both acute and chronic time scales. In this work, acute impacts are assumed to be embedded in annual mortality estimates, but air quality and health intervention planning would benefit from a quantitative understanding of the temporal dynamics of health effects. Calculations for the total losses in SLE attribute health impacts to PM2.5 sourced from anthropogenic activities, but background levels of this pollutant within the model are already at dangerous levels at several places in Northwest India. Moreover, evidence indicates no safe threshold for chronic PM2.5 exposure below which no adverse health impacts (e.g., a relative risk ≤1) would be expected [47,48], so total population health impacts quantified here are conservative estimates. Although the GAINS model considers urban and rural concentrations of PM2.5, urban fractions are held constant in this study between baseline conditions and the future. In reality, the urban population is expected to grow by an estimated 400 million between now and 2050 to more than 700 million in total [77]. As a result, changing patterns of urbanization in the country are not adequately captured, biasing health impact results downward [78]. It is important to recognize that because outdoor air pollution-related mortality was the health outcome of interest in this study, estimates are only for the population aged 30–100. India still growing quickly and has a large youth population. The total fertility rate in 2003 ranged between 2.2 children per woman in urban areas to 3.2 in rural settings, with a national average of 3.0 [79]. In coming years, a surge in the above-30 population is anticipated [42]. The above-30 population estimates applied in this study use current GAINS population totals for ease of comparability; in truth, future health impacts will be larger due to population growth. Moreover, none of the morbidity impacts of PM2.5 are addressed under current GAINS methodology despite their contribution to ill health and reduced human productivity. In considering the results of this modeling, we also acknowledge the overall health burden in South Asia and some unique risks to its population. A 2007 study found that people native to India, Pakistan, Bangladesh, Nepal and Sri Lanka typically die from heart disease five to ten years earlier than those from other ethnic groups [80]. Moreover, there is increasing evidence that traditional risk factors for acute myocardial infarction (including obesity, high blood pressure, elevated cholesterol levels, and diabetes) attack the cardiovascular system more aggressively in this population. These findings, and those of related studies on lung function and underlying environmental health determinants ([81,82]), suggest that the application of cohort study results estimating premature mortality from cardiovascular disease due to PM2.5 exposure may underestimate the health risk for particular populations in South Asia.

4.8. Policy Implications The integrated exposure-response functions have important implications for air quality management and public health policy. Under the assumption of biological saturation and a plateau of health risk, areas with more polluted air at baseline are less amenable to improvements in health relative to their cleaner counterparts, because marginal health impacts are highest at relatively low levels of PM2.5 [83]. In contrast to the current log-linear, all-cause mortality risk function, the new methods indicate that once a threshold of PM2.5 has been passed, health impacts accrue at a declining rate (Figure5). As a result, in areas experiencing high levels of pollution, only substantial improvements in air quality will produce desired dividends in improvements to population health. Nevertheless, application of the integrated exposure-response functions estimated a global total of 3.2 million Int. J. Environ. Res. Public Health 2019, 16, 60 15 of 20 premature deaths and identified of outdoor air pollution as the ninth leading risk factor for disease and early death in 2010 [2]. More broadly, trends in air pollution and associated health impacts are only a component of the entire burden of disease in India. As the country continues to progress through the epidemiologic transition, the health sector is tasked with addressing threats to well-being from a number of sources. Better estimates of the long-term impact of PM2.5 exposure will allow for improved public health planning, and could motivate policies to further improve air quality. It is unclear how helpful the quantification of specific-cause mortality risks is in the absence of baseline incidence data at a more local level. Moreover, in communicating health impact assessments to policymakers, premature mortality estimates linked to air pollution should be put in adequate context. This study did not consider lag effects in relating health outcomes to pollutant exposures, as long-term exposures are related to average impacts on SLE. In addition to cause-specific mortality estimates, recent studies posit age-specific exposure-response functions for ischemic heart disease as a function of chronic exposure to fine particulate matter [20]. Incorporation of age-specific exposure-response functions was outside of the scope of this study, but would be useful in determining the proportional impacts of cause-specific mortality from air pollution exposures. Because ischemic heart disease comprises such a large percentage of air pollution-related disease, application of these new functions could have important implications for population health impact assessments in this region.

5. Conclusions We applied integrated exposure-response functions to estimate the cause-specific mortality risks associated with ambient PM2.5 exposures in India in 2030 using Greenhouse Gas-Air Pollution Interactions and Synergies (GAINS) model projections. Losses in statistical life expectancy (SLE) were calculated based on population-weighted exposure estimates, relative risk estimates for mortality (all-cause and cause-specific), and baseline national mortality rates. Calculated reductions in SLE were aggregated and weighted using national age-adjusted, cause-specific mortality rates. In our 3 modeling, 2030 PM2.5 pollution in India reached an annual mean of 74 µg/m , nearly eight times the corresponding World Health Organization air quality guideline. The average loss in SLE was 32.5 months (95% Confidence Interval: 29.7–35.2, regional range: 8.5–42.0), compared to an average of 53.7 months (95% CI: 46.3–61.1) using methods currently applied in GAINS and similar models. Although these impacts are large, our transparent methods likely underestimate the total health burden caused by PM2.5 exposures due to model assumptions on minimum age thresholds of pollution effects and a limited subset of health endpoints analyzed. Application of the revised exposure-response models suggest that the most polluted areas in India will reap major health benefits only with substantial improvements in air quality.

Author Contributions: V.S.L., M.A., and W.S.; methodology, M.A., W.S., and V.S.L.; software, M.A., W.S., and V.S.L.; validation, V.S.L., M.A., and W.S.; formal analysis, V.S.L.; investigation, V.S.L.; resources, M.A. and W.S.; data curation, W.S. and V.S.L.; writing—original draft preparation, V.S.L.; writing—review and editing, V.S.L., M.A., and W.S.; visualization, V.S.L.; supervision, M.A. and W.S.; project administration, M.A.; funding acquisition, M.A., W.S., and V.S.L. Funding: This research was funded by the U.S. National Science Foundation, grant numbers DGE-0549407 (Integrative Graduate Education and Research Traineeship titled “Vulnerability and Sustainability in Coupled Human-Natural Systems”) and 1242069 (International Institute for Applied Systems Analysis Young Scientists Summer Program). Acknowledgments: Thanks to Chris Heyes, Fabian Wagner, Jens Borken, Jonathan Patz, Tracey Holloway, Mutlu Özdogan, Mari Palta, Marty Kanarek, Greg Nemet, Megan Zuelsdorff, Dawn Magnusson, and Ajay Limaye for their contributions to this work. We also acknowledge the support of Carmen Marcos-Sanchez, Tanja Huber, Aleks Cofala, Brian Fath, Sarah Evans, Pin Pin Oh, Hem Dholakia, Angan Sengupta, Anubhab Pattanayak, Olha Danylo, Warren Sanderson, and Joanne Linnerooth-Bayer. Conﬂicts of Interest: The authors declare no conﬂict of interest. Int. J. Environ. Res. Public Health 2019, 16, 60 16 of 20

References

1. World Health Organization. Quantiﬁcation of Health Effects of Exposure to Air Pollution; World Health Organization Regional Ofﬁce for Europe: Bonn, Germany, 2001. 2. Lim, S.S.; Vos, T.; Flaxman, A.D.; Danaei, G.; Shibuya, K.; Adair-Rohani, H.; AlMazroa, M.A.; Amann, M.; Anderson, H.R.; Andrews, K.G.; et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990–2010: A systematic analysis for the Global Burden of Disease Study 2010. Lancet 2012, 380, 2224–2260. [CrossRef] 3. Cohen, A.J.; Anderson, H.R.; Ostro, B.; Pandey, K.D.; Krzyzanowski, M.; Künzli, N.; Gutschmidt, K.; Pope III, C.; Romieu, I.; Samet, J.M. Urban air pollution. Comp. Quantif. Health Risks Glob. Reg. Burd. Dis. Due Sel. Major Risk Factors 2004, 2, 1353–1433. 4. Purohit, P.; Amann, M.; Mathur, R.; Gupta, I.; Marwah, S.; Verma, V.; Bertok, I.; Borken-Kleefeld, J.; Chambers, A.; Cofala, J.; et al. GAINS-Asia. Scenarios for Cost-Effective Control of Air Pollution and Greenhouse Gases in India; International Institute for Applied Systems Analysis (IIASA): Laxenburg, Austria, 2010. 5. Chowdhury, Z.; Zheng, M.; Schauer, J.J.; Sheesley, R.J.; Salmon, L.G.; Cass, G.R.; Russell, A.G. Speciation

of ambient ﬁne organic carbon particles and source apportionment of PM2.5 in Indian cities. J. Geophys. Res. Atmos. 2007, 112, D15303. [CrossRef] 6. Bruce, N.; Perez-Padilla, R.; Albalak, R. Indoor air pollution in developing countries: A major environmental and public health challenge. Bull. World Health Organ. 2000, 78, 1078–1092. [PubMed] 7. Smith, K. National Burden of Disease in India from Indoor Air Pollution. Proc. Natl. Acad. Sci. USA 2000, 97, 13286–13293. [CrossRef][PubMed] 8. Collaborators, I.S.-L.D.B.I.A.P. The impact of Air Pollution on Deaths, Disease Burden, and Life Expectancy Across the States of India: The Global Burden of Disease Study 2017. Lancet Planet Health 2018.[CrossRef] 9. Brauer, M.; Amann, M.; Burnett, R.T.; Cohen, A.; Dentener, F.; Ezzati, M.; Henderson, S.B.; Krzyzanowski, M.; Martin, R.V.; Van Dingenen, R.; et al. Exposure Assessment for Estimation of the Global Burden of Disease Attributable to Outdoor Air Pollution. Environ. Sci. Technol. 2012, 46, 652–660. [CrossRef] 10. World Health Organization. Air Quality Guidelines: Global Update 2005: Particulate Matter, Ozone, Nitrogen Dioxide, and Sulfur Dioxide; World Health Organization: Copenhagen, Denmark, 2006; ISBN 978-92-890-2192-0. 11. Tonne, C.; Salmon, M.; Sanchez, M.; Sreekanth, V.; Bhogadi, S.; Sambandam, S.; Balakrishnan, K.; Kinra, S.;

Marshall, J.D. Integrated assessment of exposure to PM2.5 in South India and its relation with cardiovascular risk: Design of the CHAI observational cohort study. Int. J. Hyg. Environ. Health 2017, 220, 1081–1088. [CrossRef] 12. Amann, M.; Bertok, I.; Borken, J.; Chambers, A.; Cofala, J.; Dentener, F.; Heyes, C.; Hoglund, L.; Klimont, Z.; Purohit, P.; et al. GAINS-Asia: A Tool to Combat Air Pollution and Climate Change Simultaneously; Methodology; International Institute for Applied Systems Analysis (IIASA): Laxenburg, Austria, 2008. 13. Hoek, G.; Krishnan, R.M.; Beelen, R.; Peters, A.; Ostro, B.; Brunekreef, B.; Kaufman, J.D. Long-term air pollution exposure and cardio-respiratory mortality: A review. Environ. Health 2013, 12, 43. [CrossRef] [PubMed] 14. Seaton, A.; Godden, D.; MacNee, W.; Donaldson, K. Particulate air pollution and acute health effects. Lancet 1995, 345, 176–178. [CrossRef] 15. Ambrose, J.A.; Barua, R.S. The pathophysiology of cigarette smoking and cardiovascular disease. J. Am. Coll. Cardiol. 2004, 43, 1731–1737. [CrossRef][PubMed] 16. Strulovici-Barel, Y.; Omberg, L.; O’Mahony, M.; Gordon, C.; Hollmann, C.; Tilley, A.E.; Salit, J.; Mezey, J.; Harvey, B.-G.; Crystal, R.G. Threshold of Biologic Responses of the Small Airway Epithelium to Low Levels of Tobacco Smoke. Am. J. Respir. Crit. Care Med. 2010, 182, 1524–1532. [CrossRef][PubMed] 17. Smith, K.R.; Mehta, S. The burden of disease from indoor air pollution in developing countries: Comparison of estimates. Int. J. Hyg. Environ. Health 2003, 206, 279–289. [CrossRef][PubMed] 18. Malmqvist, E.; Oudin, A.; Pascal, M.; Medina, S. Choices Behind Numbers: A Review of the Major Air Pollution Health Impact Assessments in Europe. Curr. Environ. Health Rep. 2018, 5, 34–43. [CrossRef] [PubMed] 19. Brunekreef, B. Air pollution and life expectancy: Is there a relation? Occup. Environ. Med. 1997, 54, 781. [CrossRef][PubMed] Int. J. Environ. Res. Public Health 2019, 16, 60 17 of 20

20. Burnett, R.T.; Pope, C.A., III; Ezzati, M.; Olives, C.; Lim, S.S.; Mehta, S.; Shin, H.H.; Singh, G.; Hubbell, B.; Brauer, M.; et al. An Integrated Risk Function for Estimating the Global Burden of Disease Attributable to Ambient Fine Particulate Matter Exposure. Environ. Health Perspect. 2014, 122, 397. [CrossRef][PubMed] 21. Ministry of Environment, Forests, and Climate Change (MOEFCC). Draft National Clean Air Plan. Available online: http://envfor.nic.in/sites/default/files/NCAP%20with%20annex-ilovepdf-compressed. pdf (accessed on 7 December 2018). 22. Gordon, T.; Balakrishnan, K.; Dey, S.; Rajagopalan, S.; Thornburg, J.; Thurston, G.; Agrawal, A.; Collman, G.; Guleria, R.; Limaye, S.; et al. Air pollution health research priorities for India: Perspectives of the Indo-U.S. Communities of Researchers. Environ. Int. 2018, 119, 100–108. [CrossRef][PubMed] 23. Cohen, A.J.; Ross Anderson, H.; Ostro, B.; Pandey, K.D.; Krzyzanowski, M.; Künzli, N.; Gutschmidt, K.; Pope, A.; Romieu, I.; Samet, J.M.; et al. The Global Burden of Disease Due to Outdoor Air Pollution. J. Toxicol. Environ. Health A 2005, 68, 1301–1307. [CrossRef] 24. Pope III, C.A.; Burnett, R.T.; Thun, M.J.; Calle, E.E.; Krewski, D.; Ito, K.; Thurston, G.D. Lung cancer, cardiopulmonary mortality, and long-term exposure to fine particulate air pollution. J. Am. Med. Assoc. 2002, 287, 1132–1141. [CrossRef] 25. Walter, A. Time-Series Analysis of Air Pollution and Mortality: A Statistical Review; U.S. Environmental Protection Agency: Washington, DC, USA, 2004. 26. Smith, K.R.; Frumkin, H.; Balakrishnan, K.; Butler, C.D.; Chafe, Z.A.; Fairlie, I.; Kinney, P.; Kjellstrom, T.; Mauzerall, D.L.; McKone, T.E.; et al. Energy and Human Health. Annu. Rev. Public Health 2013, 34, 159–188. [CrossRef] 27. Central Pollution Control Board. National Ambient Air Quality Status & Trends in India. Available online: http://cpcb.nic.in/openpdffile.php?id= UmVwb3J0RmlsZXMvMzJfMTQ1ODEyNjU5MV9OZXdJdGVtXzE5Ml9OQUFRU1RJLnBkZg (accessed on 2 May 2018). 28. Limaye, V.S.; Knowlton, K.; Sarkar, S.; Ganguly, P.S.; Pingle, S.; Dutta, P.; M, L.S.; Tiwari, A.; Solanki, B.; Shah, C.; et al. Development of Ahmedabad’s Air Information and Response (AIR) Plan to Protect Public Health. Int. J. Environ. Res. Public Health 2018, 15, 1460. [CrossRef][PubMed] 29. Dey, S.; Di Girolamo, L.; van Donkelaar, A.; Tripathi, S.; Gupta, T.; Mohan, M. Variability of outdoor fine

particulate (PM2.5) concentration in the Indian subcontinent: A remote sensing approach. Remote Sens. Environ. 2012, 127, 153–161. [CrossRef]

30. Chowdhury, S.; Dey, S. Cause-speciﬁc premature death from ambient PM2.5 exposure in India: Estimate adjusted for baseline mortality. Environ. Int. 2016, 91, 283–290. [CrossRef][PubMed]

31. Kumar, N.; Chu, A.; Foster, A. An empirical relationship between PM2.5 and aerosol optical depth in Delhi Metropolitan. Atmos. Environ. 2007, 41, 4492–4503. [CrossRef][PubMed] 32. Central Pollution Control Board. National Ambient Air Quality Standards. Available online: http://cpcb. nic.in/air-quality-standard/ (accessed on 7 December 2018).

33. Apte, J.S.; Marshall, J.D.; Cohen, A.J.; Brauer, M. Addressing Global Mortality from Ambient PM2.5. Environ. Sci. Technol. 2015, 49, 8057–8066. [CrossRef][PubMed] 34. Pope III, C.A.; Burnett, R.T.; Turner, M.C.; Cohen, A.; Krewski, D.; Jerrett, M.; Gapstur, S.M.; Thun, M.J. Lung Cancer and Cardiovascular Disease Mortality Associated with Ambient Air Pollution and Cigarette Smoke: Shape of the Exposure–Response Relationships. Environ. Health Perspect. 2011, 119, 1616–1621. [CrossRef] [PubMed] 35. McCracken, J.P.; Wellenius, G.A.; Bloomfield, G.S.; Brook, R.D.; Tolunay, H.E.; Dockery, D.W.; Rabadan-Diehl, C.; Checkley, W.; Rajagopalan, S. Household Air Pollution from Solid Fuel Use. Glob. Heart 2012, 7, 223–234. [CrossRef][PubMed] 36. US Environmental Protection Agency. Environmental Benefits Mapping and Analysis Program (BenMAP) 4.0; EPA: Washington, DC, USA, 2010. 37. Brook, R.D.; Rajagopalan, S.; Pope, C.A.; Brook, J.R.; Bhatnagar, A.; Diez-Roux, A.V.; Holguin, F.; Hong, Y.; Luepker, R.V.; Mittleman, M.A.; et al. Particulate Matter Air Pollution and Cardiovascular Disease: An Update to the Scientific Statement from the American Heart Association. Circulation 2010, 121, 2331–2378. [CrossRef] Int. J. Environ. Res. Public Health 2019, 16, 60 18 of 20

38. Wu, S.; Deng, F.; Wang, X.; Wei, H.; Shima, M.; Huang, J.; Lv, H.; Hao, Y.; Zheng, C.; Qin, Y.; et al. Association of lung function in a panel of young healthy adults with various chemical components of ambient ﬁne particulate air pollution in Beijing, China. Atmos. Environ. 2013, 77, 873–884. [CrossRef] 39. Ministry of Health and Family Welfare. Report of the Steering Committee on Air Pollution and Health-Related Issues; Ministry of Health and Family Welfare: Delhi, India, 2015. 40. Pant, P.; Guttikunda, S.K.; Peltier, R.E. Exposure to particulate matter in India: A synthesis of ﬁndings and future directions. Environ. Res. 2016, 147, 480–496. [CrossRef][PubMed] 41. World Health Organization. Global Health Risks: Mortality and Burden of Disease Attributable to Selected Major Risks; World Health Organization, Ed.; World Health Organization: Geneva, Switzerland, 2009; ISBN 978-92-4-156387-1. 42. U.S. Census Bureau. International Programs—Information Gateway—U.S. Census Bureau. Available online: http://www.census.gov/population/international/data/idb/informationGateway.php (accessed on 8 August 2012). 43. Crouse, D.L.; Peters, P.A.; van Donkelaar, A.; Goldberg, M.S.; Villeneuve, P.J.; Brion, O.; Khan, S.; Atari, D.O.; Jerrett, M.; Pope, C.A.; et al. Risk of Nonaccidental and Cardiovascular Mortality in Relation to Long-term Exposure to Low Concentrations of Fine Particulate Matter: A Canadian National-Level Cohort Study. Environ. Health Perspect. 2012, 120, 708–714. [CrossRef][PubMed] 44. Di, Q.; Wang, Y.; Zanobetti, A.; Wang, Y.; Koutrakis, P.; Choirat, C.; Dominici, F.; Schwartz, J.D. Air Pollution and Mortality in the Medicare Population. N. Engl. J. Med. 2017, 376, 2513–2522. [CrossRef][PubMed] 45. Fann, N.; Baker, K.R.; Chan, E.A.W.; Eyth, A.; Macpherson, A.; Miller, E.; Snyder, J. Assessing Human Health

PM2.5 and Ozone Impacts from U.S. Oil and Natural Gas Sector Emissions in 2025. Environ. Sci. Technol. 2018, 52, 8095–8103. [CrossRef][PubMed] 46. Schwartz, J.D.; Wang, Y.; Kloog, I.; Yitshak-Sade, M.; Dominici, F.; Zanobetti, A. Estimating the Effects of

PM2.5 on Life Expectancy Using Causal Modeling Methods. Environ. Health Perspect. 2018, 126, 127002. [CrossRef] 47. U.S. Environmental Protection Agency, Ofﬁce of Air Quality Planning and Standards Health and Environmental Impact Division, Air Beneﬁt-Cost Group. Summary of Expert Opinions on the Existence

of a Threshold in the Concentration-Response Function for PM2.5-related Mortality. Available online: http://www.epa.gov/ttn/ecas/regdata/Benefits/thresholdstsd.pdf (accessed on 10 August 2012). 48. Correia, A.W.; Pope, C.A.; Dockery, D.W.; Wang, Y.; Ezzati, M.; Dominici, F. Effect of Air Pollution Control on Life Expectancy in the United States. Epidemiology 2013, 24, 23–31. [CrossRef] 49. Shin, H.H.; Cohen, A.J.; Pope, C.A., III; Ezzati, M.; Lim, S.S.; Hubbell, B.J.; Burnett, R.T. Meta-analysis methods to estimate the shape and uncertainty in the association between long-term exposure to ambient fine particulate matter and cause-specific mortality over the global concentration range. Risk Anal. 2016, 36, 1813–1825. [CrossRef] 50. Cox, D.R. Regression Models and Life-Tables. J. R. Stat. Soc. 1972, 34, 187–220. [CrossRef] 51. Ramanathan, V.; Feng, Y. Air pollution, greenhouse gases and climate change: Global and regional perspectives. Atmos. Environ. 2009, 43, 37–50. [CrossRef] 52. Pommier, M.; Fagerli, H.; Gauss, M.; Simpson, D.; Sharma, S.; Sinha, V.; Ghude, S.D.; Landgren, O.; Nyiri, A.;

Wind, P. Impact of regional climate change and future emission scenarios on surface O3 and PM2.5 over India. Atmos. Chem. Phys. 2018, 18, 103–127. [CrossRef] 53. Mickley, L.J.; Jacob, D.J.; Field, B.D.; Rind, D. Effects of future climate change on regional air pollution episodes in the United States. Geophys. Res. Lett. 2004, 31, L24103. [CrossRef] 54. Jacob, D.J.; Winner, D.A. Effect of climate change on air quality. Atmos. Environ. 2009, 43, 51–63. [CrossRef] 55. Rosenfeld, D. Suppression of Rain and Snow by Urban and Industrial Air Pollution. Science 2000, 287, 1793–1796. [CrossRef][PubMed] 56. Givati, A.; Rosenfeld, D. Quantifying precipitation suppression due to air pollution. J. Appl. Meteorol. 2004, 43, 1038–1056. [CrossRef] 57. Joint Task Force on the Health, Aspects of Air Pollution; Executive Body for the Convention on Long-range Transboundary Air Pollution. Modelling and Assessment of the Health Impact of Particulate Matter and Ozone; World Health Organization/European Centre for Environment and Health: Bonn, Germany, 2004. 58. Vaupel, J.W.; Yashin, A.I. Heterogeneity’s Ruses: Some Surprising Effects of Selection on Population Dynamics. Am. Stat. 1985, 39, 176–185. Int. J. Environ. Res. Public Health 2019, 16, 60 19 of 20

59. World Health Organization. Years of Life Lost (Percentage of Total). Available online: https://www.who. int/whosis/whostat2006YearsOfLifeLost.pdf (accessed on 10 August 2012).

60. Apte, J.S.; Brauer, M.; Cohen, A.J.; Ezzati, M.; Pope, C.A. Ambient PM2.5 Reduces Global and Regional Life Expectancy. Environ. Sci. Technol. Lett. 2018, 5, 546–551. [CrossRef] 61. Berkowitz, B.; Muyskens, J.; Sharma, M.; Ulmanu, M. How Many Years Do We Lose to the Air We Breathe? Available online: https://www.washingtonpost.com/graphics/2018/national/health-science/lost-years/ (accessed on 8 December 2018). 62. Ghude, S.D.; Chate, D.; Jena, C.; Beig, G.; Kumar, R.; Barth, M.; Pﬁster, G.; Fadnavis, S.; Pithani, P. Premature

mortality in India due to PM2.5 and ozone exposure. Geophys. Res. Lett. 2016, 43, 4650–4658. [CrossRef] 63. Greenstone, M.; Nilekani, J.; Pande, R.; Ryan, N.; Sudarshan, A.; Sugathan, A. Lower pollution, longer lives: Life expectancy gains if India reduced particulate matter pollution. Econ. Polit. Wkly. 2015, 50, 40–46. 64. Health Effects Institute. Burden of Disease Attributable to Major Air Pollution Sources in India. GBD MAPS Working Group, Summary for Policy Makers; Health Effects Institute (HEI): Boston, MA, USA, 2018. 65. Ramanathan, K. The Energy and Resources Institute National Energy Map for India: Technology Vision 2030 (Summary for Policy-Makers); The Energy and Resources Institute: New Delhi, India, 2003. 66. Bush, K.F.; Luber, G.; Kotha, S.R.; Dhaliwal, R.S.; Kapil, V.; Pascual, M.; Brown, D.G.; Frumkin, H.; Dhiman, R.C.; Hess, J.; et al. Impacts of Climate Change on Public Health in India: Future Research Directions. Environ. Health Perspect. 2011, 119, 765–770. [CrossRef] 67. Anenberg, S.C.; Belova, A.; Brandt, J.; Fann, N.; Greco, S.; Guttikunda, S.; Heroux, M.; Hurley, F.; Krzyzanowski, M.; Medina, S. Survey of ambient air pollution health risk assessment tools. Risk Anal. 2016, 36, 1718–1736. [CrossRef] 68. Fann, N.; Kim, S.-Y.; Olives, C.; Sheppard, L. Estimated Changes in Life Expectancy and Adult Mortality

Resulting from Declining PM2.5 Exposures in the Contiguous United States: 1980–2010. Environ. Health Perspect. 2017, 125, 097003. [CrossRef]

69. Maji, K.J.; Arora, M.; Dikshit, A.K. Burden of disease attributed to ambient PM2.5 and PM10 exposure in 190 cities in China. Environ. Sci. Pollut. Res. 2017, 24, 11559–11572. [CrossRef][PubMed]

70. Heo, J.; Adams, P.J.; Gao, H.O. Reduced-form modeling of public health impacts of inorganic PM2.5 and precursor emissions. Atmos. Environ. 2016, 137, 80–89. [CrossRef] 71. Nasari, M.M.; Szyszkowicz, M.; Chen, H.; Crouse, D.; Turner, M.C.; Jerrett, M.; Pope, C.A.; Hubbell, B.; Fann, N.; Cohen, A. A class of non-linear exposure-response models suitable for health impact assessment applicable to large cohort studies of ambient air pollution. Air Qual. Atmos. Health 2016, 9, 961–972. [CrossRef][PubMed] 72. Laumbach, R.J.; Kipen, H.M. Respiratory health effects of air pollution: Update on biomass smoke and trafﬁc pollution. J. Allergy Clin. Immunol. 2012, 129, 3–11. [CrossRef][PubMed] 73. Guarnieri, M.; Balmes, J.R. Outdoor air pollution and asthma. Lancet 2014, 383, 1581–1592. [CrossRef]

74. Jain, V.; Dey, S.; Chowdhury, S. Ambient PM2.5 exposure and premature mortality burden in the holy city Varanasi, India. Environ. Pollut. 2017, 226, 182–189. [CrossRef] 75. Balakrishnan, K.; Cohen, A.; Smith, K.R. Addressing the Burden of Disease Attributable to Air Pollution in India: The Need to Integrate Across Household and Ambient Air Pollution Exposures. Environ. Health Perspect. 2014, 122, A6–A7. [CrossRef] 76. Samet, J.M.; Zeger, S.L.; Dominici, F.; Curriero, F.; Coursac, I.; Dockery, D.W.; Schwartz, J.; Zanobetti, A. The National Morbidity, Mortality, and Air Pollution Study. Part II: Morbidity and Mortality from Air Pollution in the United States; Heallth Effects Institute: Boston, MA, USA, 2000; Volume 94, pp. 5–79. 77. Revi, A. Climate change risk: An adaptation and mitigation agenda for Indian cities. Environ. Urban. 2008, 20, 207–229. [CrossRef] 78. Nijman, J. India’s Urban Challenge. Eurasian Geogr. Econ. 2012, 53, 7–20. [CrossRef] 79. Ofﬁce of the Registrar General and Census Commissioner, Ministry of Home Affairs (Government of India). Census of India—Vital Statistics—Sample Registration System. Available online: http://www.censusindia. gov.in/vital_statistics/Vital_Rates/Vital_rates.aspx (accessed on 28 August 2012). 80. Joshi, P.; Islam, S.; Pais, P.; Reddy, S.; Dorairaj, P.; Kazmi, K.; Pandey, M.R.; Haque, S.; Mendis, S.; Rangarajan, S. Risk factors for early myocardial infarction in South Asians compared with individuals in other countries. J. Am. Med. Assoc. 2007, 297, 286–294. [CrossRef][PubMed] Int. J. Environ. Res. Public Health 2019, 16, 60 20 of 20

81. Duong, M.; Islam, S.; Rangarajan, S.; Teo, K.; O’Byrne, P.M.; Schünemann, H.J.; Igumbor, E.; Chifamba, J.; Liu, L.; Li, W.; et al. Global differences in lung function by region (PURE): An international, community-based prospective study. Lancet Respir. Med. 2013, 1, 599–609. [CrossRef] 82. Fulambarker, A. Comparison of Pulmonary Function in Immigrant vs. US-Born Asian Indians. CHEST J. 2010, 137, 1398. [CrossRef][PubMed] 83. Smith, K.R.; Peel, J.L. Mind the Gap. Environ. Health Perspect. 2010, 118, 1643–1645. [CrossRef][PubMed]

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